A significant challenge facing healthcare professionals endeavoring to maintain a patient's health is to convince the patient of potential medical outcomes stemming from the patient's behavior and lifestyle. Indeed, not a few health experts have ranked lifestyle as an even greater determinant of health and wellness, long term at least, than genetics, heredity, and family histories combined. To be convinced, though, the patient must accurately perceive the potential outcomes, the probabilities of the potential outcomes, and the factors that make each more or less likely.
With respect to even a single patient, providing a statistically-defensible predictions of possible health outcomes typically requires the collating and assessment of health-related medical and lifestyle information. Such information, even individual-specific information, can be generated over long periods and, usually, is extraordinarily voluminous. Typically, the information is only obtainable from disparate sources.
Today there is not an effective and efficient technique for providing lifestyle alternatives simulations. It is thus often difficult to provide to the patient a compelling picture that lays out the need to alter one or more lifestyle factors. Many, if not most, patients typically exist in at least a partial state of denial over the importance of such factors. This tends to be especially true with younger patients noted for misconstruing youth as absolute invulnerability. The absence of techniques for making complex mathematical and statistical evaluations of such information also precludes opportunities to discover unknown maladies, whether created by nature or caused by man-made factors. That is there are no effective and efficient mechanisms for generating predictive analyses based on lifestyle and medical histories possibly prevents the uncovering of hidden maladies. Moreover, there do not yet exist effective and efficient mechanisms for generating models of wellness based on such factors, let alone any mechanism for fine tuning such models based upon iteratively-applied feedback.